Hire AI engineers
Hire dedicated AI engineers without guessing the role.
Devlyn maps your AI bottleneck to one of eight production roles, sends a vetted shortlist within 48 hours, and lets you prove fit through a two-week paid trial in your real codebase.
What is Devlyn’s AI engineer hiring model?
Devlyn is for product teams that need one dedicated AI-native engineer matched to a precise production role, with public pricing, a 48-hour shortlist, and trial proof in the buyer’s workflow.
Role clarity
“AI engineer” is a search query, not a job spec.
The right hire depends on the bottleneck. Model behavior, private-data retrieval, agent tool use, app UX, platform reliability, and security all need different proof during the trial.
Forward-Deployed AI Engineer
AI feature stuck between demo and production?
Workflow map, feature-flagged slice, eval baseline.
AI Application Engineer
Need product UI on top of LLMs?
Working feature slice, streaming UI, telemetry.
LLM Engineer
Model quality, routing, evals, or fine-tuning unclear?
Eval dataset, scorecard, cost/latency report.
RAG & Context Engineer
Model must answer from your private data?
Retrieval baseline, citation design, relevance eval set.
Agentic Workflow Engineer
Need tool-using agents with approvals and retries?
Workflow graph, first agent slice, trace logs.
AI Platform Engineer
Every team is building separate AI wrappers?
Gateway/platform slice, trace convention, cost baseline.
AI Security Engineer
AI security risk blocking release?
Threat model, attack scenarios, security backlog.
Data Scientist
Need decisions, forecasts, experiments, or causal analysis?
Data quality audit, metric tree, baseline analysis.
How to choose
Choose by the work the engineer must own.
If users need a product feature
Start with AI Application Engineer, unless the rollout is still ambiguous enough to need Forward-Deployed ownership.
If answers need private data
Start with RAG & Context Engineer. Add AI Security Engineer when permissions or leakage risk blocks launch.
If quality is subjective
Start with LLM Engineer to create evals, routing, model comparison, and cost/latency visibility.
If teams are duplicating AI wrappers
Start with AI Platform Engineer to build shared gateways, eval hubs, observability, and cost controls.
Process
From broad need to precise shortlist.
30-minute role scope
Map the AI workflow, current stack, first deliverable, security boundaries, seniority, and the role that should own the work.
2-3 vetted engineers
Receive a short list with matching rationale. The goal is fewer names with stronger fit, not resume volume.
Paid trial in your codebase
The selected engineer works inside your repo, rituals, issue tracker, and review process so fit is judged by real work.
Continue, replace, pause, or scale
Continue month-to-month, request a free replacement, pause without a long lock-in, or add adjacent roles.
Vetting standard
Screened for production AI.
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Pricing
Public role pricing.
Supervised delivery for clear implementation work.
Independent feature ownership for production AI work.
High-judgment ownership for ambiguous or risky AI delivery.
Trial proof
Judge real work before continuing.
Pull request card
The trial should create inspectable code, not a private demo.
Eval report
Quality, retrieval, routing, or workflow behavior should have a baseline.
Architecture decision record
Tradeoffs should be written clearly enough for your team to maintain.
Workflow map
The engineer should make scope, actors, systems, and failure paths visible.
Security and IP
Access is scoped before onboarding.
NDA, IP assignment, repository access, communication boundaries, and data rules are clarified before the engineer starts. Devlyn works inside buyer-controlled systems and avoids unverified compliance claims.
FAQ
AI engineer hiring questions.
What does it mean to hire AI engineers through Devlyn?
Devlyn scopes your AI bottleneck, maps it to one of eight production AI roles, shortlists two or three vetted engineers within 48 hours, and proves fit through a two-week paid trial.
Is “AI engineer” too broad?
Often, yes. AI app UX, RAG, LLM evals, agents, platform, security, forward-deployed rollout, and data science are different ownership models.
Can Devlyn support remote teams?
Yes. Engagements are planned around meaningful overlap for interviews, standups, reviews, and escalation.
Can the trial happen in our actual repo?
Yes. The trial is designed around your codebase or approved data environment so the decision is based on real work.
How much does it cost?
Junior starts at $2,500/mo, mid at $3,500/mo, and senior at $4,500/mo.
How is this different from AI consulting?
Devlyn places a dedicated engineer into your team. It is staffing around a role, not a strategy deck or an external agency project.
Can we start with one role and add more?
Yes. Start with the bottleneck, prove fit, then add adjacent roles if the roadmap needs more specialists.
What if we do not know the role?
Use the role scope call. The expected output is a role recommendation and first-proof plan, even if Devlyn is not the right fit.